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The Alpha Magnetic Spectrometer (AMS) is a high-precision particle detector onboard the International Space Station containing six different subdetectors. The Transition Radiation Detector and Electromagnetic Calorimeter (ECAL) are used to separate electrons/positrons from the abundant cosmic-ray proton background. The positron flux measured in space by AMS falls with a power law which unexpectedly softens above 25 GeV and then hardens above 280 GeV. Several theoretical models try to explain these phenomena, and a purer measurement of positrons at higher energies is needed to help test them. The currently used methods to reject the proton background at high energies involve extrapolating shower features from the ECAL to use as inputs for boosted decision tree and likelihood classifiers. We present a new approach for particle identification with the AMS ECAL using deep learning (DL). By taking the energy deposition within all the ECAL cells as an input and treating them as pixels in an image-like format, we train an MLP, a CNN, and multiple ResNets and Convolutional vision Transformers (CvTs) as shower classifiers. Proton rejection performance is evaluated using Monte Carlo (MC) events and ISS data separately. For MC, using events with a reconstructed energy between 0.2 - 2 TeV, at 90% electron accuracy, the proton rejection power of our CvT model is more than 5 times that of the other DL models. Similarly, for ISS data with a reconstructed energy between 50 - 70 GeV, the proton rejection power of our CvT model is more than 2.5 times that of the other DL models., Comment: 19 pages, 11 Figures

The Alpha Magnetic Spectrometer (AMS) is constantly exposed to harsh condition on the ISS. As such, there is a need to constantly monitor and perform adjustments to ensure the AMS operates safely and efficiently. With the addition of the Upgraded Tracker Thermal Pump System, the legacy monitoring interface was no longer suitable for use. This paper describes the new AMS Monitoring Interface (AMI). The AMI is built with state-of-the-art time series database and analytics software. It uses a custom feeder program to process AMS Raw Data as time series data points, feeds them into InfluxDB databases, and uses Grafana as a visualization tool. It follows modern design principles, allowing client CPUs to handle the processing work, distributed creation of AMI dashboards, and up-to-date security protocols. In addition, it offers a more simple way of modifying the AMI and allows the use of APIs to automate backup and synchronization. The new AMI has been in use since January 2020 and was a crucial component in remote shift taking during the COVID-19 pandemic., Comment: 16 pages, 15 figures, submitted to Nuclear Instruments and Methods in Physics Research Section A